Value of quantitative airspace disease measured on chest CT and chest radiography at initial diagnosis compared to clinical variables for prediction of severe COVID-19.
| Autor: | Jung HM; University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, United States., Yang R; University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, United States., Gefter WB; University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, United States., Ghesu FC; Siemens Healthineers, Digital Technology and Innovation, Princeton, New Jersey, United States., Mailhe B; Siemens Healthineers, Digital Technology and Innovation, Princeton, New Jersey, United States., Mansoor A; Siemens Healthineers, Digital Technology and Innovation, Princeton, New Jersey, United States., Grbic S; Siemens Healthineers, Digital Technology and Innovation, Princeton, New Jersey, United States., Comaniciu D; Siemens Healthineers, Digital Technology and Innovation, Princeton, New Jersey, United States., Vogt S; Siemens Healthineers, X-Ray Products, Malvern, Pennsylvania, United States., Mortani Barbosa EJ Jr; University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, United States. |
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| Jazyk: | angličtina |
| Zdroj: | Journal of medical imaging (Bellingham, Wash.) [J Med Imaging (Bellingham)] 2022 May; Vol. 9 (3), pp. 034003. Date of Electronic Publication: 2022 Jun 17. |
| DOI: | 10.1117/1.JMI.9.3.034003 |
| Abstrakt: | Purpose: Rapid prognostication of COVID-19 patients is important for efficient resource allocation. We evaluated the relative prognostic value of baseline clinical variables (CVs), quantitative human-read chest CT (qCT), and AI-read chest radiograph (qCXR) airspace disease (AD) in predicting severe COVID-19. Approach: We retrospectively selected 131 COVID-19 patients (SARS-CoV-2 positive, March to October, 2020) at a tertiary hospital in the United States, who underwent chest CT and CXR within 48 hr of initial presentation. CVs included patient demographics and laboratory values; imaging variables included qCT volumetric percentage AD (POv) and qCXR area-based percentage AD (POa), assessed by a deep convolutional neural network. Our prognostic outcome was need for ICU admission. We compared the performance of three logistic regression models: using CVs known to be associated with prognosis (model I), using a dimension-reduced set of best predictor variables (model II), and using only age and AD (model III). Results: 60/131 patients required ICU admission, whereas 71/131 did not. Model I performed the poorest ( AUC = 0.67 [0.58 to 0.76]; accuracy = 77 % ). Model II performed the best ( AUC = 0.78 [0.71 to 0.86]; accuracy = 81 % ). Model III was equivalent ( AUC = 0.75 [0.67 to 0.84]; accuracy = 80 % ). Both models II and III outperformed model I ( AUC difference = 0.11 [0.02 to 0.19], p = 0.01 ; AUC difference = 0.08 [0.01 to 0.15], p = 0.04 , respectively). Model II and III results did not change significantly when POv was replaced by POa. Conclusions: Severe COVID-19 can be predicted using only age and quantitative AD imaging metrics at initial diagnosis, which outperform the set of CVs. Moreover, AI-read qCXR can replace qCT metrics without loss of prognostic performance, promising more resource-efficient prognostication. (© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE).) |
| Databáze: | MEDLINE |
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